Overview

Dataset statistics

Number of variables24
Number of observations36646
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory192.0 B

Variable types

Categorical3
Unsupported1
Numeric20

Warnings

100M et +.1 has constant value "0" Constant
Unnamed: 0 has a high cardinality: 36646 distinct values High cardinality
Unnamed: 2 has a high cardinality: 104 distinct values High cardinality
éligibles is highly correlated with éligibles.1High correlation
3M et + is highly correlated with 3M et +.1High correlation
8M et + is highly correlated with 8M et +.1High correlation
éligibles.1 is highly correlated with éligiblesHigh correlation
3M et +.1 is highly correlated with 3M et +High correlation
8M et +.1 is highly correlated with 8M et +High correlation
éligibles.2 is highly correlated with 3M et +.2 and 3 other fieldsHigh correlation
3M et +.2 is highly correlated with éligibles.2 and 3 other fieldsHigh correlation
8M et +.2 is highly correlated with éligibles.2 and 3 other fieldsHigh correlation
30M et +.2 is highly correlated with éligibles.2 and 3 other fieldsHigh correlation
100M et +.2 is highly correlated with éligibles.2 and 3 other fieldsHigh correlation
éligibles.3 is highly correlated with 3M et +.3 and 3 other fieldsHigh correlation
3M et +.3 is highly correlated with éligibles.3 and 3 other fieldsHigh correlation
8M et +.3 is highly correlated with éligibles.3 and 3 other fieldsHigh correlation
30M et +.3 is highly correlated with éligibles.3 and 3 other fieldsHigh correlation
100M et +.3 is highly correlated with éligibles.3 and 3 other fieldsHigh correlation
Unnamed: 3 is highly skewed (γ1 = 125.4784865) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysis Unsupported
3M et + has 4051 (11.1%) zeros Zeros
8M et + has 7913 (21.6%) zeros Zeros
30M et + has 18350 (50.1%) zeros Zeros
100M et + has 33793 (92.2%) zeros Zeros
3M et +.1 has 4208 (11.5%) zeros Zeros
8M et +.1 has 8278 (22.6%) zeros Zeros
30M et +.1 has 19602 (53.5%) zeros Zeros
éligibles.2 has 35527 (96.9%) zeros Zeros
3M et +.2 has 35527 (96.9%) zeros Zeros
8M et +.2 has 35527 (96.9%) zeros Zeros
30M et +.2 has 35527 (96.9%) zeros Zeros
100M et +.2 has 35643 (97.3%) zeros Zeros
éligibles.3 has 34361 (93.8%) zeros Zeros
3M et +.3 has 34361 (93.8%) zeros Zeros
8M et +.3 has 34361 (93.8%) zeros Zeros
30M et +.3 has 34361 (93.8%) zeros Zeros
100M et +.3 has 34361 (93.8%) zeros Zeros

Reproduction

Analysis started2021-02-18 21:09:41.724791
Analysis finished2021-02-18 21:10:25.255625
Duration43.53 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct36646
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size286.4 KiB
60375
 
1
60437
 
1
52476
 
1
30100
 
1
79118
 
1
Other values (36641)
36641 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters183230
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36646 ?
Unique (%)100.0%

Sample

1st row01001
2nd row01002
3rd row01004
4th row01005
5th row01006
ValueCountFrequency (%)
603751
 
< 0.1%
604371
 
< 0.1%
524761
 
< 0.1%
301001
 
< 0.1%
791181
 
< 0.1%
131131
 
< 0.1%
430341
 
< 0.1%
531681
 
< 0.1%
831321
 
< 0.1%
393301
 
< 0.1%
Other values (36636)36636
> 99.9%
2021-02-18T22:10:25.507296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
603751
 
< 0.1%
628961
 
< 0.1%
523071
 
< 0.1%
245471
 
< 0.1%
502611
 
< 0.1%
170521
 
< 0.1%
111641
 
< 0.1%
614081
 
< 0.1%
631921
 
< 0.1%
644851
 
< 0.1%
Other values (36636)36636
> 99.9%

Most occurring characters

ValueCountFrequency (%)
124306
13.3%
223657
12.9%
023284
12.7%
319369
10.6%
517728
9.7%
417481
9.5%
616386
8.9%
715676
8.6%
813853
7.6%
911130
6.1%
Other values (2)360
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number182870
99.8%
Uppercase Letter360
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
124306
13.3%
223657
12.9%
023284
12.7%
319369
10.6%
517728
9.7%
417481
9.6%
616386
9.0%
715676
8.6%
813853
7.6%
911130
6.1%
ValueCountFrequency (%)
B236
65.6%
A124
34.4%

Most occurring scripts

ValueCountFrequency (%)
Common182870
99.8%
Latin360
 
0.2%

Most frequent character per script

ValueCountFrequency (%)
124306
13.3%
223657
12.9%
023284
12.7%
319369
10.6%
517728
9.7%
417481
9.6%
616386
9.0%
715676
8.6%
813853
7.6%
911130
6.1%
ValueCountFrequency (%)
B236
65.6%
A124
34.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII183230
100.0%

Most frequent character per block

ValueCountFrequency (%)
124306
13.3%
223657
12.9%
023284
12.7%
319369
10.6%
517728
9.7%
417481
9.5%
616386
8.9%
715676
8.6%
813853
7.6%
911130
6.1%
Other values (2)360
 
0.2%

Unnamed: 1
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size286.4 KiB

Unnamed: 2
Categorical

HIGH CARDINALITY

Distinct104
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size286.4 KiB
62
 
895
02
 
816
80
 
782
76
 
745
57
 
730
Other values (99)
32678 

Length

Max length3
Median length2
Mean length2.003629318
Min length2

Characters and Unicode

Total characters73425
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row01
2nd row01
3rd row01
4th row01
5th row01
ValueCountFrequency (%)
62895
 
2.4%
02816
 
2.2%
80782
 
2.1%
76745
 
2.0%
57730
 
2.0%
21706
 
1.9%
14705
 
1.9%
60692
 
1.9%
27675
 
1.8%
59650
 
1.8%
Other values (94)29250
79.8%
2021-02-18T22:10:25.697871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
62895
 
2.4%
02816
 
2.2%
80782
 
2.1%
76745
 
2.0%
57730
 
2.0%
21706
 
1.9%
14705
 
1.9%
60692
 
1.9%
27675
 
1.8%
59650
 
1.8%
Other values (94)29250
79.8%

Most occurring characters

ValueCountFrequency (%)
28820
12.0%
18391
11.4%
58110
11.0%
78095
11.0%
68042
11.0%
07062
9.6%
36994
9.5%
86672
9.1%
46648
9.1%
94231
5.8%
Other values (2)360
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number73065
99.5%
Uppercase Letter360
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
28820
12.1%
18391
11.5%
58110
11.1%
78095
11.1%
68042
11.0%
07062
9.7%
36994
9.6%
86672
9.1%
46648
9.1%
94231
5.8%
ValueCountFrequency (%)
B236
65.6%
A124
34.4%

Most occurring scripts

ValueCountFrequency (%)
Common73065
99.5%
Latin360
 
0.5%

Most frequent character per script

ValueCountFrequency (%)
28820
12.1%
18391
11.5%
58110
11.1%
78095
11.1%
68042
11.0%
07062
9.7%
36994
9.6%
86672
9.1%
46648
9.1%
94231
5.8%
ValueCountFrequency (%)
B236
65.6%
A124
34.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII73425
100.0%

Most frequent character per block

ValueCountFrequency (%)
28820
12.0%
18391
11.4%
58110
11.0%
78095
11.0%
68042
11.0%
07062
9.6%
36994
9.5%
86672
9.1%
46648
9.1%
94231
5.8%
Other values (2)360
 
0.5%

Unnamed: 3
Real number (ℝ≥0)

SKEWED

Distinct4246
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1038.543197
Minimum0
Maximum1761772
Zeros2
Zeros (%)< 0.1%
Memory size286.4 KiB
2021-02-18T22:10:25.788584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q1120
median248
Q3587
95-th percentile3177.75
Maximum1761772
Range1761772
Interquartile range (IQR)467

Descriptive statistics

Standard deviation10743.38235
Coefficient of variation (CV)10.34466586
Kurtosis19861.93725
Mean1038.543197
Median Absolute Deviation (MAD)161
Skewness125.4784865
Sum38058454
Variance115420264.4
MonotocityNot monotonic
2021-02-18T22:10:25.891591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81123
 
0.3%
60122
 
0.3%
88122
 
0.3%
83117
 
0.3%
90116
 
0.3%
52115
 
0.3%
78115
 
0.3%
56114
 
0.3%
70113
 
0.3%
82112
 
0.3%
Other values (4236)35477
96.8%
ValueCountFrequency (%)
02
< 0.1%
11
 
< 0.1%
44
< 0.1%
51
 
< 0.1%
62
< 0.1%
ValueCountFrequency (%)
17617721
< 0.1%
4853621
< 0.1%
3273831
< 0.1%
2987541
< 0.1%
2619131
< 0.1%

éligibles
Real number (ℝ≥0)

HIGH CORRELATION

Distinct742
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9676065601
Minimum0
Maximum1
Zeros261
Zeros (%)0.7%
Memory size286.4 KiB
2021-02-18T22:10:26.022947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.841
Q10.995
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.127879942
Coefficient of variation (CV)0.1321610944
Kurtosis36.77812021
Mean0.9676065601
Median Absolute Deviation (MAD)0
Skewness-5.851331794
Sum35458.91
Variance0.01635327956
MonotocityNot monotonic
2021-02-18T22:10:26.160970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125383
69.3%
0.999565
 
1.5%
0.997515
 
1.4%
0.998502
 
1.4%
0.996452
 
1.2%
0.995405
 
1.1%
0.994351
 
1.0%
0.993334
 
0.9%
0.991273
 
0.7%
0.992262
 
0.7%
Other values (732)7604
 
20.7%
ValueCountFrequency (%)
0261
0.7%
0.0041
 
< 0.1%
0.0051
 
< 0.1%
0.0072
 
< 0.1%
0.0081
 
< 0.1%
ValueCountFrequency (%)
125383
69.3%
0.999565
 
1.5%
0.998502
 
1.4%
0.997515
 
1.4%
0.996452
 
1.2%

3M et +
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1001
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6913758391
Minimum0
Maximum1
Zeros4051
Zeros (%)11.1%
Memory size286.4 KiB
2021-02-18T22:10:26.270358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.42325
median0.891
Q30.988
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.56475

Descriptive statistics

Standard deviation0.3721253855
Coefficient of variation (CV)0.5382389207
Kurtosis-0.757399475
Mean0.6913758391
Median Absolute Deviation (MAD)0.109
Skewness-0.9398393482
Sum25336.159
Variance0.1384773025
MonotocityNot monotonic
2021-02-18T22:10:26.372525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15724
 
15.6%
04051
 
11.1%
0.997358
 
1.0%
0.998356
 
1.0%
0.996330
 
0.9%
0.995325
 
0.9%
0.999313
 
0.9%
0.994295
 
0.8%
0.993285
 
0.8%
0.992262
 
0.7%
Other values (991)24347
66.4%
ValueCountFrequency (%)
04051
11.1%
0.0013
 
< 0.1%
0.00213
 
< 0.1%
0.00324
 
0.1%
0.00435
 
0.1%
ValueCountFrequency (%)
15724
15.6%
0.999313
 
0.9%
0.998356
 
1.0%
0.997358
 
1.0%
0.996330
 
0.9%

8M et +
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1001
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5348155597
Minimum0
Maximum1
Zeros7913
Zeros (%)21.6%
Memory size286.4 KiB
2021-02-18T22:10:26.476683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02325
median0.676
Q30.932
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.90875

Descriptive statistics

Standard deviation0.4063775416
Coefficient of variation (CV)0.7598461455
Kurtosis-1.658953964
Mean0.5348155597
Median Absolute Deviation (MAD)0.316
Skewness-0.2553374219
Sum19598.851
Variance0.1651427063
MonotocityNot monotonic
2021-02-18T22:10:26.618948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07913
 
21.6%
12810
 
7.7%
0.995201
 
0.5%
0.997191
 
0.5%
0.993191
 
0.5%
0.998172
 
0.5%
0.994169
 
0.5%
0.996162
 
0.4%
0.992161
 
0.4%
0.999143
 
0.4%
Other values (991)24533
66.9%
ValueCountFrequency (%)
07913
21.6%
0.0018
 
< 0.1%
0.00233
 
0.1%
0.00368
 
0.2%
0.00469
 
0.2%
ValueCountFrequency (%)
12810
7.7%
0.999143
 
0.4%
0.998172
 
0.5%
0.997191
 
0.5%
0.996162
 
0.4%

30M et +
Real number (ℝ≥0)

ZEROS

Distinct1001
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2406472739
Minimum0
Maximum1
Zeros18350
Zeros (%)50.1%
Memory size286.4 KiB
2021-02-18T22:10:26.740168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.47
95-th percentile0.929
Maximum1
Range1
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.3201325868
Coefficient of variation (CV)1.330297998
Kurtosis-0.3587323524
Mean0.2406472739
Median Absolute Deviation (MAD)0
Skewness1.017362346
Sum8818.76
Variance0.1024848732
MonotocityNot monotonic
2021-02-18T22:10:26.845866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018350
50.1%
1314
 
0.9%
0.00683
 
0.2%
0.00380
 
0.2%
0.00575
 
0.2%
0.00763
 
0.2%
0.00862
 
0.2%
0.00960
 
0.2%
0.00260
 
0.2%
0.00460
 
0.2%
Other values (991)17439
47.6%
ValueCountFrequency (%)
018350
50.1%
0.00151
 
0.1%
0.00260
 
0.2%
0.00380
 
0.2%
0.00460
 
0.2%
ValueCountFrequency (%)
1314
0.9%
0.99920
 
0.1%
0.99819
 
0.1%
0.99722
 
0.1%
0.99622
 
0.1%

100M et +
Real number (ℝ≥0)

ZEROS

Distinct859
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04872700977
Minimum0
Maximum1
Zeros33793
Zeros (%)92.2%
Memory size286.4 KiB
2021-02-18T22:10:26.950901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.54675
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1926658895
Coefficient of variation (CV)3.953985488
Kurtosis14.62049878
Mean0.04872700977
Median Absolute Deviation (MAD)0
Skewness3.9949053
Sum1785.65
Variance0.03712014499
MonotocityNot monotonic
2021-02-18T22:10:27.056034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033793
92.2%
153
 
0.1%
0.00125
 
0.1%
0.00221
 
0.1%
0.98117
 
< 0.1%
0.99417
 
< 0.1%
0.99917
 
< 0.1%
0.00316
 
< 0.1%
0.97215
 
< 0.1%
0.98614
 
< 0.1%
Other values (849)2658
 
7.3%
ValueCountFrequency (%)
033793
92.2%
0.00125
 
0.1%
0.00221
 
0.1%
0.00316
 
< 0.1%
0.00411
 
< 0.1%
ValueCountFrequency (%)
153
0.1%
0.99917
 
< 0.1%
0.99812
 
< 0.1%
0.99711
 
< 0.1%
0.99611
 
< 0.1%

éligibles.1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct749
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9669435409
Minimum0
Maximum1
Zeros267
Zeros (%)0.7%
Memory size286.4 KiB
2021-02-18T22:10:27.177699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.836
Q10.995
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.1295782678
Coefficient of variation (CV)0.1340081012
Kurtosis35.69784038
Mean0.9669435409
Median Absolute Deviation (MAD)0
Skewness-5.772849645
Sum35434.613
Variance0.0167905275
MonotocityNot monotonic
2021-02-18T22:10:27.285550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125332
69.1%
0.999570
 
1.6%
0.997515
 
1.4%
0.998498
 
1.4%
0.996450
 
1.2%
0.995404
 
1.1%
0.994352
 
1.0%
0.993330
 
0.9%
0.991272
 
0.7%
0267
 
0.7%
Other values (739)7656
 
20.9%
ValueCountFrequency (%)
0267
0.7%
0.0041
 
< 0.1%
0.0051
 
< 0.1%
0.0072
 
< 0.1%
0.0081
 
< 0.1%
ValueCountFrequency (%)
125332
69.1%
0.999570
 
1.6%
0.998498
 
1.4%
0.997515
 
1.4%
0.996450
 
1.2%

3M et +.1
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1001
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6774981717
Minimum0
Maximum1
Zeros4208
Zeros (%)11.5%
Memory size286.4 KiB
2021-02-18T22:10:28.056400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.374
median0.8755
Q30.985
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.611

Descriptive statistics

Standard deviation0.3757549016
Coefficient of variation (CV)0.5546212777
Kurtosis-0.8944526471
Mean0.6774981717
Median Absolute Deviation (MAD)0.1245
Skewness-0.8660662376
Sum24827.598
Variance0.1411917461
MonotocityNot monotonic
2021-02-18T22:10:28.168230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15573
 
15.2%
04208
 
11.5%
0.998328
 
0.9%
0.997320
 
0.9%
0.996301
 
0.8%
0.995297
 
0.8%
0.994273
 
0.7%
0.993263
 
0.7%
0.999259
 
0.7%
0.992251
 
0.7%
Other values (991)24573
67.1%
ValueCountFrequency (%)
04208
11.5%
0.0014
 
< 0.1%
0.00217
 
< 0.1%
0.00325
 
0.1%
0.00437
 
0.1%
ValueCountFrequency (%)
15573
15.2%
0.999259
 
0.7%
0.998328
 
0.9%
0.997320
 
0.9%
0.996301
 
0.8%

8M et +.1
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1001
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5104984446
Minimum0
Maximum1
Zeros8278
Zeros (%)22.6%
Memory size286.4 KiB
2021-02-18T22:10:28.270391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.014
median0.618
Q30.915
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.901

Descriptive statistics

Standard deviation0.4054091372
Coefficient of variation (CV)0.7941437266
Kurtosis-1.693928437
Mean0.5104984446
Median Absolute Deviation (MAD)0.368
Skewness-0.1543132338
Sum18707.726
Variance0.1643565685
MonotocityNot monotonic
2021-02-18T22:10:28.371548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08278
 
22.6%
12704
 
7.4%
0.995176
 
0.5%
0.993173
 
0.5%
0.997167
 
0.5%
0.994144
 
0.4%
0.998141
 
0.4%
0.992140
 
0.4%
0.996133
 
0.4%
0.989124
 
0.3%
Other values (991)24466
66.8%
ValueCountFrequency (%)
08278
22.6%
0.00112
 
< 0.1%
0.00240
 
0.1%
0.00377
 
0.2%
0.00478
 
0.2%
ValueCountFrequency (%)
12704
7.4%
0.999104
 
0.3%
0.998141
 
0.4%
0.997167
 
0.5%
0.996133
 
0.4%

30M et +.1
Real number (ℝ≥0)

ZEROS

Distinct1000
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1970353927
Minimum0
Maximum1
Zeros19602
Zeros (%)53.5%
Memory size286.4 KiB
2021-02-18T22:10:28.472562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.383
95-th percentile0.824
Maximum1
Range1
Interquartile range (IQR)0.383

Descriptive statistics

Standard deviation0.2856737063
Coefficient of variation (CV)1.449859857
Kurtosis0.343162353
Mean0.1970353927
Median Absolute Deviation (MAD)0
Skewness1.255330042
Sum7220.559
Variance0.08160946648
MonotocityNot monotonic
2021-02-18T22:10:28.578340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019602
53.5%
1254
 
0.7%
0.00390
 
0.2%
0.00689
 
0.2%
0.00578
 
0.2%
0.00273
 
0.2%
0.00172
 
0.2%
0.00871
 
0.2%
0.00470
 
0.2%
0.00969
 
0.2%
Other values (990)16178
44.1%
ValueCountFrequency (%)
019602
53.5%
0.00172
 
0.2%
0.00273
 
0.2%
0.00390
 
0.2%
0.00470
 
0.2%
ValueCountFrequency (%)
1254
0.7%
0.9982
 
< 0.1%
0.9975
 
< 0.1%
0.9967
 
< 0.1%
0.99521
 
0.1%

100M et +.1
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size286.4 KiB
0
36646 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters36646
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
036646
100.0%
2021-02-18T22:10:28.745348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-02-18T22:10:28.793958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
036646
100.0%

Most occurring characters

ValueCountFrequency (%)
036646
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36646
100.0%

Most frequent character per category

ValueCountFrequency (%)
036646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common36646
100.0%

Most frequent character per script

ValueCountFrequency (%)
036646
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII36646
100.0%

Most frequent character per block

ValueCountFrequency (%)
036646
100.0%

éligibles.2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct456
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02183206898
Minimum0
Maximum1
Zeros35527
Zeros (%)96.9%
Memory size286.4 KiB
2021-02-18T22:10:28.853611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1351048095
Coefficient of variation (CV)6.188364905
Kurtosis38.02063187
Mean0.02183206898
Median Absolute Deviation (MAD)0
Skewness6.251729216
Sum800.058
Variance0.01825330955
MonotocityNot monotonic
2021-02-18T22:10:28.957971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035527
96.9%
124
 
0.1%
0.00224
 
0.1%
0.00123
 
0.1%
0.96912
 
< 0.1%
0.98311
 
< 0.1%
0.98110
 
< 0.1%
0.9910
 
< 0.1%
0.99110
 
< 0.1%
0.97510
 
< 0.1%
Other values (446)985
 
2.7%
ValueCountFrequency (%)
035527
96.9%
0.00123
 
0.1%
0.00224
 
0.1%
0.0039
 
< 0.1%
0.0044
 
< 0.1%
ValueCountFrequency (%)
124
0.1%
0.9998
 
< 0.1%
0.9984
 
< 0.1%
0.9971
 
< 0.1%
0.9966
 
< 0.1%

3M et +.2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct456
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02183206898
Minimum0
Maximum1
Zeros35527
Zeros (%)96.9%
Memory size286.4 KiB
2021-02-18T22:10:29.062022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1351048095
Coefficient of variation (CV)6.188364905
Kurtosis38.02063187
Mean0.02183206898
Median Absolute Deviation (MAD)0
Skewness6.251729216
Sum800.058
Variance0.01825330955
MonotocityNot monotonic
2021-02-18T22:10:29.166358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035527
96.9%
124
 
0.1%
0.00224
 
0.1%
0.00123
 
0.1%
0.96912
 
< 0.1%
0.98311
 
< 0.1%
0.98110
 
< 0.1%
0.9910
 
< 0.1%
0.99110
 
< 0.1%
0.97510
 
< 0.1%
Other values (446)985
 
2.7%
ValueCountFrequency (%)
035527
96.9%
0.00123
 
0.1%
0.00224
 
0.1%
0.0039
 
< 0.1%
0.0044
 
< 0.1%
ValueCountFrequency (%)
124
0.1%
0.9998
 
< 0.1%
0.9984
 
< 0.1%
0.9971
 
< 0.1%
0.9966
 
< 0.1%

8M et +.2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct456
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02183206898
Minimum0
Maximum1
Zeros35527
Zeros (%)96.9%
Memory size286.4 KiB
2021-02-18T22:10:29.270399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1351048095
Coefficient of variation (CV)6.188364905
Kurtosis38.02063187
Mean0.02183206898
Median Absolute Deviation (MAD)0
Skewness6.251729216
Sum800.058
Variance0.01825330955
MonotocityNot monotonic
2021-02-18T22:10:29.374726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035527
96.9%
124
 
0.1%
0.00224
 
0.1%
0.00123
 
0.1%
0.96912
 
< 0.1%
0.98311
 
< 0.1%
0.98110
 
< 0.1%
0.9910
 
< 0.1%
0.99110
 
< 0.1%
0.97510
 
< 0.1%
Other values (446)985
 
2.7%
ValueCountFrequency (%)
035527
96.9%
0.00123
 
0.1%
0.00224
 
0.1%
0.0039
 
< 0.1%
0.0044
 
< 0.1%
ValueCountFrequency (%)
124
0.1%
0.9998
 
< 0.1%
0.9984
 
< 0.1%
0.9971
 
< 0.1%
0.9966
 
< 0.1%

30M et +.2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct456
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02183206898
Minimum0
Maximum1
Zeros35527
Zeros (%)96.9%
Memory size286.4 KiB
2021-02-18T22:10:29.478200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1351048095
Coefficient of variation (CV)6.188364905
Kurtosis38.02063187
Mean0.02183206898
Median Absolute Deviation (MAD)0
Skewness6.251729216
Sum800.058
Variance0.01825330955
MonotocityNot monotonic
2021-02-18T22:10:29.582132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035527
96.9%
124
 
0.1%
0.00224
 
0.1%
0.00123
 
0.1%
0.96912
 
< 0.1%
0.98311
 
< 0.1%
0.98110
 
< 0.1%
0.9910
 
< 0.1%
0.99110
 
< 0.1%
0.97510
 
< 0.1%
Other values (446)985
 
2.7%
ValueCountFrequency (%)
035527
96.9%
0.00123
 
0.1%
0.00224
 
0.1%
0.0039
 
< 0.1%
0.0044
 
< 0.1%
ValueCountFrequency (%)
124
0.1%
0.9998
 
< 0.1%
0.9984
 
< 0.1%
0.9971
 
< 0.1%
0.9966
 
< 0.1%

100M et +.2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct443
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019102576
Minimum0
Maximum1
Zeros35643
Zeros (%)97.3%
Memory size286.4 KiB
2021-02-18T22:10:29.685899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.125983642
Coefficient of variation (CV)6.595112724
Kurtosis44.07515468
Mean0.019102576
Median Absolute Deviation (MAD)0
Skewness6.703412722
Sum700.033
Variance0.01587187806
MonotocityNot monotonic
2021-02-18T22:10:29.789945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
035643
97.3%
0.00224
 
0.1%
0.00121
 
0.1%
120
 
0.1%
0.00312
 
< 0.1%
0.9839
 
< 0.1%
0.999
 
< 0.1%
0.9699
 
< 0.1%
0.9869
 
< 0.1%
0.9259
 
< 0.1%
Other values (433)881
 
2.4%
ValueCountFrequency (%)
035643
97.3%
0.00121
 
0.1%
0.00224
 
0.1%
0.00312
 
< 0.1%
0.0044
 
< 0.1%
ValueCountFrequency (%)
120
0.1%
0.9997
 
< 0.1%
0.9984
 
< 0.1%
0.9966
 
< 0.1%
0.9951
 
< 0.1%

éligibles.3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct854
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03339745129
Minimum0
Maximum1
Zeros34361
Zeros (%)93.8%
Memory size286.4 KiB
2021-02-18T22:10:29.893728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.156
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.154606052
Coefficient of variation (CV)4.629276965
Kurtosis23.544453
Mean0.03339745129
Median Absolute Deviation (MAD)0
Skewness4.920467655
Sum1223.883
Variance0.0239030313
MonotocityNot monotonic
2021-02-18T22:10:29.997993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034361
93.8%
129
 
0.1%
0.00120
 
0.1%
0.00313
 
< 0.1%
0.00711
 
< 0.1%
0.00411
 
< 0.1%
0.00910
 
< 0.1%
0.0029
 
< 0.1%
0.8859
 
< 0.1%
0.0059
 
< 0.1%
Other values (844)2164
 
5.9%
ValueCountFrequency (%)
034361
93.8%
0.00120
 
0.1%
0.0029
 
< 0.1%
0.00313
 
< 0.1%
0.00411
 
< 0.1%
ValueCountFrequency (%)
129
0.1%
0.9993
 
< 0.1%
0.9983
 
< 0.1%
0.9973
 
< 0.1%
0.9962
 
< 0.1%

3M et +.3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct854
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03339745129
Minimum0
Maximum1
Zeros34361
Zeros (%)93.8%
Memory size286.4 KiB
2021-02-18T22:10:30.102427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.156
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.154606052
Coefficient of variation (CV)4.629276965
Kurtosis23.544453
Mean0.03339745129
Median Absolute Deviation (MAD)0
Skewness4.920467655
Sum1223.883
Variance0.0239030313
MonotocityNot monotonic
2021-02-18T22:10:30.206550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034361
93.8%
129
 
0.1%
0.00120
 
0.1%
0.00313
 
< 0.1%
0.00711
 
< 0.1%
0.00411
 
< 0.1%
0.00910
 
< 0.1%
0.0029
 
< 0.1%
0.8859
 
< 0.1%
0.0059
 
< 0.1%
Other values (844)2164
 
5.9%
ValueCountFrequency (%)
034361
93.8%
0.00120
 
0.1%
0.0029
 
< 0.1%
0.00313
 
< 0.1%
0.00411
 
< 0.1%
ValueCountFrequency (%)
129
0.1%
0.9993
 
< 0.1%
0.9983
 
< 0.1%
0.9973
 
< 0.1%
0.9962
 
< 0.1%

8M et +.3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct854
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03339745129
Minimum0
Maximum1
Zeros34361
Zeros (%)93.8%
Memory size286.4 KiB
2021-02-18T22:10:30.310532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.156
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.154606052
Coefficient of variation (CV)4.629276965
Kurtosis23.544453
Mean0.03339745129
Median Absolute Deviation (MAD)0
Skewness4.920467655
Sum1223.883
Variance0.0239030313
MonotocityNot monotonic
2021-02-18T22:10:30.414465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034361
93.8%
129
 
0.1%
0.00120
 
0.1%
0.00313
 
< 0.1%
0.00711
 
< 0.1%
0.00411
 
< 0.1%
0.00910
 
< 0.1%
0.0029
 
< 0.1%
0.8859
 
< 0.1%
0.0059
 
< 0.1%
Other values (844)2164
 
5.9%
ValueCountFrequency (%)
034361
93.8%
0.00120
 
0.1%
0.0029
 
< 0.1%
0.00313
 
< 0.1%
0.00411
 
< 0.1%
ValueCountFrequency (%)
129
0.1%
0.9993
 
< 0.1%
0.9983
 
< 0.1%
0.9973
 
< 0.1%
0.9962
 
< 0.1%

30M et +.3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct854
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03339745129
Minimum0
Maximum1
Zeros34361
Zeros (%)93.8%
Memory size286.4 KiB
2021-02-18T22:10:30.541798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.156
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.154606052
Coefficient of variation (CV)4.629276965
Kurtosis23.544453
Mean0.03339745129
Median Absolute Deviation (MAD)0
Skewness4.920467655
Sum1223.883
Variance0.0239030313
MonotocityNot monotonic
2021-02-18T22:10:30.690150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034361
93.8%
129
 
0.1%
0.00120
 
0.1%
0.00313
 
< 0.1%
0.00711
 
< 0.1%
0.00411
 
< 0.1%
0.00910
 
< 0.1%
0.0029
 
< 0.1%
0.8859
 
< 0.1%
0.0059
 
< 0.1%
Other values (844)2164
 
5.9%
ValueCountFrequency (%)
034361
93.8%
0.00120
 
0.1%
0.0029
 
< 0.1%
0.00313
 
< 0.1%
0.00411
 
< 0.1%
ValueCountFrequency (%)
129
0.1%
0.9993
 
< 0.1%
0.9983
 
< 0.1%
0.9973
 
< 0.1%
0.9962
 
< 0.1%

100M et +.3
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct854
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03339745129
Minimum0
Maximum1
Zeros34361
Zeros (%)93.8%
Memory size286.4 KiB
2021-02-18T22:10:30.824523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.156
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.154606052
Coefficient of variation (CV)4.629276965
Kurtosis23.544453
Mean0.03339745129
Median Absolute Deviation (MAD)0
Skewness4.920467655
Sum1223.883
Variance0.0239030313
MonotocityNot monotonic
2021-02-18T22:10:30.931171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034361
93.8%
129
 
0.1%
0.00120
 
0.1%
0.00313
 
< 0.1%
0.00711
 
< 0.1%
0.00411
 
< 0.1%
0.00910
 
< 0.1%
0.0029
 
< 0.1%
0.8859
 
< 0.1%
0.0059
 
< 0.1%
Other values (844)2164
 
5.9%
ValueCountFrequency (%)
034361
93.8%
0.00120
 
0.1%
0.0029
 
< 0.1%
0.00313
 
< 0.1%
0.00411
 
< 0.1%
ValueCountFrequency (%)
129
0.1%
0.9993
 
< 0.1%
0.9983
 
< 0.1%
0.9973
 
< 0.1%
0.9962
 
< 0.1%

Interactions

2021-02-18T22:09:46.142269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.250599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.344117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.437312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.530442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.623542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.716672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.809636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.902661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:46.995094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:47.087574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:47.179564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:47.946779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:48.917137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:49.693312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:49.785987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:49.877972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:49.969878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.063693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.155633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.246284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.330893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.415603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.500073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.584317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.668812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.753592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.837990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:50.922470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.006735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.091202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.175472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.259706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.343724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.428089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.512247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.597461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.682612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.767775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.857789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:51.942489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:52.026546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:52.110427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:52.194370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:52.647133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:52.751210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:52.835500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:52.919855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.004467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.088480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.172694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.256602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.340760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.425738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.510124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.594558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.679216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.764203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.854511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:53.939458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.023910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.108348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.192562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.277478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.362131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.446754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.531245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.615710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.700211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.784784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.868953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:54.953105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.037827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.122788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.207399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.291809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.376649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.466790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.551313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.635603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.719291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.803512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.887911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:55.972430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.056573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.140290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.224517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.308103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.392093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.475879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.559749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.643862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.730501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:09:56.907825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:56.994200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.086177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.174036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.260428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.344266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.427917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.512242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.597105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.681676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.766293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.850239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:57.933818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:58.017433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:58.101246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:58.185162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:58.269405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:58.353329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:58.437257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:58.919973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:59.032459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:59.123467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:59.208288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:59.293376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:59.377787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:09:59.546334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:59.632284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:09:59.716753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:10:00.222894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:00.307061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:00.391032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:00.475279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:00.559437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:00.644049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:00.734346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:10:11.845892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:11.931464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:12.016943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:10:15.592935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:10:17.207278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:17.312030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:17.442353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:17.559406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:17.679288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:17.806986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:17.932012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.032040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.130680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.248387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.366369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.472658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.558107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.643108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.730341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.814708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.898955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:18.986615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:10:19.242566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.333898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.419328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.504073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.588938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.673398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.758218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.842873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:19.927897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.013141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.097968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.189524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.273515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.358734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.443697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.528540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.613350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.697653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.782321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.866978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:20.957199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.042326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.126880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.211598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.295983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-02-18T22:10:21.466672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.551877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.636662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.721128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.805991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.890966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:21.975787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.060436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.145063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.230605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.321603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.406168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.507340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.636889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.756447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.864947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:22.950353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.034961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.141860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.258800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.376412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.472389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.557553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.642659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.728437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.814514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:23.933930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:24.055774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:24.171910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:24.260033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-02-18T22:10:24.367359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-02-18T22:10:31.062838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-18T22:10:31.254056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-18T22:10:31.460376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-18T22:10:31.651388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-18T22:10:24.660924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-18T22:10:25.065156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3éligibles3M et +8M et +30M et +100M et +éligibles.13M et +.18M et +.130M et +.1100M et +.1éligibles.23M et +.28M et +.230M et +.2100M et +.2éligibles.33M et +.38M et +.330M et +.3100M et +.3
001001L'Abergement-Clémenciat013611.0000.4560.0520.0000.0001.0000.4560.0520.00000.00.00.00.00.00.0000.0000.0000.0000.000
101002L'Abergement-de-Varey011680.2880.0760.0240.0240.0240.2760.0590.0000.00000.00.00.00.00.00.0240.0240.0240.0240.024
201004Ambérieu-en-Bugey0175010.9980.9740.8980.4300.1980.9980.9710.8020.23200.00.00.00.00.00.1980.1980.1980.1980.198
301005Ambérieux-en-Dombes017251.0000.9930.9450.6670.0361.0000.9880.9400.66300.00.00.00.00.00.0360.0360.0360.0360.036
401006Ambléon01731.0001.0001.0001.0001.0001.0001.0001.0001.00000.00.00.00.00.01.0001.0001.0001.0001.000
501007Ambronay0112400.9820.8580.8520.7740.7500.7780.1080.1030.02500.00.00.00.00.00.7500.7500.7500.7500.750
601008Ambutrix013731.0000.2040.0000.0000.0001.0000.2040.0000.00000.00.00.00.00.00.0000.0000.0000.0000.000
701009Andert-et-Condon011991.0000.6160.5840.0000.0001.0000.6160.5840.00000.00.00.00.00.00.0000.0000.0000.0000.000
801010Anglefort015960.9640.9330.9330.9330.9330.7700.0000.0000.00000.00.00.00.00.00.9330.9330.9330.9330.933
901011Apremont011990.9790.1110.0000.0000.0000.9790.1110.0000.00000.00.00.00.00.00.0000.0000.0000.0000.000

Last rows

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3éligibles3M et +8M et +30M et +100M et +éligibles.13M et +.18M et +.130M et +.1100M et +.1éligibles.23M et +.28M et +.230M et +.2100M et +.2éligibles.33M et +.38M et +.330M et +.3100M et +.3
3663697610Koungou97667160.9970.8090.7230.1450.00.9970.8090.7230.14500.00.00.00.00.00.00.00.00.00.0
3663797611Mamoudzou976164890.9120.8590.7650.1180.00.9120.8590.7650.11800.00.00.00.00.00.00.00.00.00.0
3663897612Mtsamboro97623650.9960.8470.6140.4020.00.9960.8470.6140.40200.00.00.00.00.00.00.00.00.00.0
3663997613M'Tsangamouji97618270.9990.9380.9230.1300.00.9990.9380.9230.13000.00.00.00.00.00.00.00.00.00.0
3664097614Ouangani97623701.0000.9830.7760.2720.01.0000.9830.7760.27200.00.00.00.00.00.00.00.00.00.0
3664197615Pamandzi97626701.0000.9980.9940.5640.01.0000.9980.9940.56400.00.00.00.00.00.00.00.00.00.0
3664297616Sada97626900.9910.9650.8390.1530.00.9910.9650.8390.15300.00.00.00.00.00.00.00.00.00.0
3664397617Tsingoni97630990.9870.7080.7060.4750.00.9870.7080.7060.47500.00.00.00.00.00.00.00.00.00.0
3664497701Saint-Barthélemy97746931.0000.9360.7620.1460.01.0000.9360.7620.14600.00.00.00.00.00.00.00.00.00.0
3664597801Saint-Martin978169320.9930.8040.6680.2140.00.9930.8040.6680.21400.00.00.00.00.00.00.00.00.00.0